Over the past two decades, the National Basketball Association (NBA) has undergone a noticeable transformation in how offense is played. Modern teams emphasize spacing, three-point shooting, and analytically driven shot selection, leading to the widespread perception that today’s NBA is more “efficient” than earlier eras. While this narrative is common among fans and analysts, efficiency can increase for different reasons: league-wide strategic shifts, individual player development, or the concentration of offensive responsibility among elite players. Distinguishing between these forces requires examining shooting efficiency at multiple levels rather than relying on surface-level trends.

This project investigates how shooting efficiency has evolved in the NBA since 2000 using True Shooting Percentage (TS%), a comprehensive metric that accounts for two-point field goals, three-point field goals, and free throws. Using player-season data from the all_seasons NBA dataset, I analyze trends in efficiency over time, across player ages and career stages, and in relation to offensive workload. By combining league-level summaries, player-level relationships, team comparisons, and interactive visualizations, this analysis aims to separate broad structural changes in the league from individual patterns of development and role specialization.

Specifically, this project addresses three central questions. First, how has league-wide shooting efficiency changed over time? Second, do players become more efficient shooters as they age and progress through their careers? Finally, how is shooting efficiency related to scoring volume and usage rate? Together, these questions provide a cohesive framework for understanding not only whether efficiency has increased, but why it has increased and who benefits most from these changes.

Visualization 1: League Shooting Efficiency Over Time

I begin at the league level by examining how average true shooting percentage has changed since 2000. This time-series plot shows a clear upward trend in league-wide efficiency, with average True Shooting % rising from roughly 49–50% in the early 2000s to over 56% in recent seasons. The steady increase supports the narrative that the modern NBA is more efficient, reflecting strategic changes such as increased three-point volume, improved spacing, and analytically optimized shot selection.

Establishing this league-wide trend provides important context for the rest of the analysis. If efficiency has risen overall, the next question is whether individual players experience similar improvements as they move through their careers, or whether these gains are driven primarily by changes in who shoots and how often.

Visualization 2: Age vs Shooting Efficiency

To explore how efficiency evolves across individual careers, I next examine the relationship between age and true shooting percentage. Rather than focusing on raw scatter points alone, I use a rolling average with confidence bands to highlight the underlying trend. This visualization shows that players tend to improve steadily from their late teens into their mid-to-late 20s, where shooting efficiency peaks, before stabilizing and gradually declining in the early 30s.

This pattern suggests that shooting efficiency is closely tied to skill development and experience rather than purely physical athleticism. Players appear to refine shot selection, decision-making, and touch as they gain experience, reinforcing the idea that efficiency improves through learning and adaptation over time.

Visualization 3: Career Stages Bar Chart

This visualization examines how shooting efficiency evolves across a player’s career by grouping seasons into age-based career stages. Average true shooting percentage increases steadily from the rookie years (≤22) through early prime (23–26), peaking during players’ prime seasons (27–29). Efficiency remains relatively high into late prime (30–32) before slightly declining in veteran years (33+). While the differences between stages are modest, the overall pattern suggests that players become more efficient as they gain experience and settle into defined offensive roles, with efficiency stabilizing rather than collapsing as players age.

Visualization 4: Points Per Game vs True Shooting %

After establishing how efficiency changes over time and across careers, I turn to the relationship between shooting efficiency and scoring volume. A scatter plot of points per game versus true shooting percentage reveals a clear positive relationship. Players who score more points per game tend, on average, to be more efficient shooters.

While there is substantial variability—reflecting differences in role, shot difficulty, and play style—the upward-sloping regression line indicates that high-volume scoring does not generally come at the expense of efficiency. Instead, many of the league’s most productive scorers are also among its most efficient.

Visualization 5: Usage Rate vs Shooting Efficiency

A similar pattern emerges when examining usage rate, which measures the share of a team’s offensive possessions a player uses while on the floor. If increased offensive responsibility reduced efficiency, we would expect to observe a negative relationship between usage rate and true shooting percentage. Instead, the fitted regression line slopes slightly upward, indicating that higher-usage players are, on average, at least as efficient as lower-usage players. Because the vast majority of NBA true shooting percentages fall between roughly 45% and 65%, the y-axis is restricted to this range to emphasize meaningful variation in efficiency without allowing extreme values to dominate the visual scale.

This pattern suggests that players entrusted with higher usage are often those capable of maintaining, or even improving, efficiency under greater offensive demands. Rather than diminishing efficiency, higher usage appears to reflect coaching trust in players who can consistently convert possessions at an efficient rate.

Visualization 6: True Shooting % by Team

After examining individual player efficiency, this visualization shifts the focus to team-level context. By comparing the distributions of True Shooting Percentage across teams, this plot assesses whether organizational environment meaningfully differentiates shooting efficiency. While most teams cluster tightly around the league average, the spread within each team highlights substantial variation in individual roles and shot quality. This suggests that although team systems matter, shooting efficiency is still largely driven by player-level factors within each organization.

Visualization 7: Shiny App 1 — Filter Season & Age Range

To make team-level differences easier to interpret, this visualization ranks teams by their median True Shooting Percentage since 2000. Unlike averages, medians reduce the influence of extreme outliers and better represent a team’s typical efficiency profile. The ranking reveals that some franchises consistently operate above the league median, while others remain closer to baseline efficiency. These differences point to long-term organizational patterns in offensive philosophy, player development, and roster construction rather than short-term fluctuations.

Visualization 8: Shiny App 2 — Choose Metric vs True Shooting %

This interactive Shiny app lets viewers explore how True Shooting % relates to several player statistics—scoring (points per game), rebounding, assists, and usage rate, while also filtering out small samples using a minimum games played slider. Because the x-axis metric can be changed, the app highlights that some relationships with True Shooting % are clearer than others (for example, scoring and usage tend to show a more noticeable positive association than rebounding or assists). Overall, this tool supports the project’s broader theme that shooting efficiency is connected to player role and offensive involvement, while still showing substantial variability across individuals.

Visualization 9: Animation of League True Shooting % Over Time

The animated time-series visualization brings the project full circle by dynamically illustrating how league-wide True Shooting Percentage evolves from season to season. Watching efficiency rise gradually over time highlights that the modern NBA’s increased efficiency is not the result of a single abrupt shift or isolated season, but rather a steady, long-term trend. The animation reinforces the project’s overarching conclusion that shooting efficiency has consistently increased across the league over the past two decades. While this visualization does not identify specific causal mechanisms, the sustained upward movement is consistent with broader changes in offensive strategy, player development, and role specialization observed throughout the modern era.

Conclusion: Taken together, these results show that the rise in NBA shooting efficiency is not driven by a single factor. League-wide strategic changes have created a more efficient offensive environment, players refine their shooting skills as they gain experience, and teams increasingly concentrate offensive responsibility among players who can sustain performance under pressure. Rather than diminishing effectiveness, increased scoring and usage tend to reflect trust in elite decision-makers. This analysis focuses on player-season aggregates and does not account for defensive pressure, lineup context, or shot difficulty, which may also influence shooting outcomes. Overall, modern NBA efficiency emerges from the interaction between evolving strategy, individual development, and role specialization.

Shiny Apps https://rileygoetz.shinyapps.io/project/